Competition Law and Future Regulation of Strategic Foresight Platforms

Introduction

Strategic foresight platforms are emerging digital systems that collect, integrate and analyse large volumes of information to help businesses, governments, investors and institutions anticipate future market conditions. They may combine AI forecasting, scenario modelling, predictive analytics, economic intelligence, market signals, supply-chain information, consumer data, geopolitical indicators and recommendation systems.

Examples include platforms that predict:

• future demand and consumer behaviour;
• commodity and energy prices;
• supply-chain disruptions;
• technological adoption;
• investment opportunities;
• regulatory developments;
• competitor strategies;
• climate and geopolitical risks; and
• likely market-entry or exit opportunities.

From a competition-law perspective, the central concern is that a platform providing foresight may become more than an information intermediary. If a small number of firms control the data, models, computational infrastructure, forecasts, interfaces and distribution channels, strategic foresight itself can become a source of market power.

The traditional competition-law questions of dominance, exclusion, collusion, foreclosure, essential facilities, tying and merger control therefore acquire a predictive dimension.

1. Meaning of Strategic Foresight Platforms

A strategic foresight platform can be understood as a system that converts large amounts of present and historical information into forward-looking competitive intelligence.

Its architecture may contain:

1. Data layer – market, consumer, financial, industrial and behavioural data.
2. Signal layer – identification of weak signals and emerging trends.
3. Prediction layer – machine-learning and econometric forecasting.
4. Scenario layer – alternative future-market simulations.
5. Decision layer – recommendations concerning investment, pricing, procurement or market entry.
6. Distribution layer – dashboards, APIs, software integrations and automated alerts.

The competition problem becomes particularly important where the same undertaking controls several of these layers.

2. Why Strategic Foresight Creates Competition Concerns

Strategic foresight can generate several forms of competitive advantage.

A. Information advantage

A platform may have access to information unavailable to competitors.

B. Prediction advantage

Its superior data or models may allow it to anticipate market changes before rivals.

C. Feedback advantage

More customers generate more information, which improves predictions, attracting still more customers.

This produces a potential data–prediction–customer feedback loop.

D. Infrastructure advantage

Businesses may become dependent upon a platform's API, forecasting model or proprietary database.

E. Coordination risk

If competing firms rely upon the same predictive system, the platform could unintentionally—or deliberately—facilitate coordination.

F. Vertical foreclosure

A vertically integrated foresight provider may disadvantage competing forecasting services or downstream businesses.

3. Relevant Competition-Law Framework

Strategic foresight platforms can potentially fall within several traditional competition-law doctrines.

A. Relevant Market

The relevant market may be defined around:

• strategic intelligence services;
• predictive analytics;
• AI forecasting;
• market-data services;
• enterprise intelligence software;
• specialised forecasting for particular industries; or
• a broader digital-information ecosystem.

The correct market definition will depend on substitutability, functionality, switching costs, data requirements and customer behaviour.

4. Dominance Through Data and Predictive Capability

Possession of data alone does not necessarily establish dominance.

However, dominance may become significant where an undertaking possesses:

• unique datasets;
• real-time information;
• historical datasets unavailable elsewhere;
• proprietary prediction models;
• superior computing resources;
• network effects;
• high switching costs;
• interoperability advantages; and
• established enterprise relationships.

The important question is increasingly:

Can competitors realistically reproduce the informational and predictive capability of the incumbent?

5. Data as a Competitive Asset

Strategic foresight platforms may accumulate several categories of information:

The competitive significance may therefore lie not in any single dataset but in data aggregation and combination.

6. Network Effects

A foresight platform can exhibit indirect network effects.

More users → more information → better predictions → greater customer attraction → more users.

This may create a self-reinforcing competitive advantage.

If the platform becomes sufficiently entrenched, competitors may face difficulty acquiring enough customers to generate comparable datasets.

7. Algorithmic Forecasting and Competition

AI-based forecasting creates an unusual competition problem.

Suppose several competitors subscribe to the same forecasting system and receive predictions concerning:

• future demand;
• expected prices;
• capacity;
• inventories;
• consumer behaviour; or
• likely competitor responses.

The platform may effectively become an information intermediary among competitors.

The competition-law question is then whether the system merely improves independent decision-making or facilitates concerted conduct.

8. Algorithmic Collusion

Strategic foresight platforms could facilitate coordination in two principal ways.

Explicit coordination

The platform intentionally exchanges competitively sensitive information among competitors.

Algorithmic coordination

Competitors independently use the same predictive system, whose recommendations cause parallel conduct.

The second situation is more legally difficult because parallel behaviour alone does not necessarily establish an unlawful agreement.

Authorities may therefore examine:

• communications between competitors;
• design of the platform;
• data inputs;
• common algorithms;
• contractual restrictions;
• recommendation mechanisms;
• knowledge of competitors' use of the system; and
• whether the platform deliberately facilitated coordination.

9. Case Law

1. United States v. Airline Tariff Publishing Co. (1994)

The U.S. Department of Justice challenged an airline fare-information system in which airlines could use a computerized system to communicate and monitor pricing information.

Competition-law significance

The case demonstrates how information technology can transform apparently independent pricing decisions into coordinated conduct.

Relevance to foresight platforms

A future strategic-foresight system providing competitors with highly sensitive predictive information could raise analogous concerns where the system facilitates:

• monitoring;
• signalling;
• price coordination; or
• strategic alignment.

The case is particularly relevant to algorithmic information exchange.

10. T-Mobile Netherlands BV v Raad van Bestuur NMa (CJEU, 2009)

The case concerned an exchange of information among competitors in the mobile telecommunications sector.

The Court recognised that information exchange may infringe competition law where it reduces uncertainty concerning competitors' future market behaviour.

Relevance

Strategic foresight platforms are inherently concerned with future behaviour.

Information concerning expected:

• prices;
• capacity;
• investments;
• market entry;
• production;
• demand; or
• commercial strategies

can therefore create substantial competition concerns when exchanged among competitors.

11. Eturas v Lietuvos Respublikos Konkurencijos Taryba (CJEU, 2016)

This case involved an online booking platform through which a common electronic system imposed restrictions affecting discounts offered by participating travel agencies.

Importance

The judgment demonstrates that an electronic platform can constitute an important mechanism through which coordinated conduct is implemented.

Application to foresight platforms

A strategic foresight platform might similarly become a coordination mechanism if it:

• standardises competitive parameters;
• transmits commercially sensitive recommendations;
• imposes common rules; or
• enables competitors to observe and respond to one another.

The technological form of the system does not remove ordinary competition-law principles.

12. Google Shopping (European Commission, 2017; General Court, 2021)

The European Commission found that Google had favoured its own comparison-shopping service in general search results.

The General Court substantially upheld the Commission's infringement finding, while addressing aspects of the Commission's reasoning.

Relevance to foresight platforms

A dominant foresight platform could potentially discriminate between:

• its own forecasting service;
• third-party forecasting providers;
• competing data providers; and
• downstream businesses.

Possible conduct could include:

• self-preferencing;
• preferential API access;
• discriminatory ranking;
• withholding important data;
• inferior interoperability for competitors.

Thus, the search-ranking problem can evolve into a prediction-ranking problem.

13. Microsoft v Commission (General Court, 2007)

The Microsoft litigation concerned, among other matters, interoperability and the ability of competitors to compete effectively with a dominant software platform.

Relevance

Strategic foresight systems may become embedded within:

• enterprise-resource planning;
• financial software;
• cloud systems;
• supply-chain management;
• business intelligence platforms.

If competitors cannot obtain necessary interoperability information, dominance at the platform layer could potentially be leveraged into adjacent forecasting or analytics markets.

14. Bronner v Mediaprint (CJEU, 1998)

The case concerned the circumstances in which refusal of access to infrastructure by a dominant undertaking can amount to an abuse.

The Court established demanding conditions for compulsory access.

Relevance

A dominant strategic-foresight platform might control:

• unique predictive datasets;
• proprietary forecasting infrastructure;
• critical APIs;
• specialised data feeds; or
• technical interfaces.

A competitor seeking access could attempt to characterise such infrastructure as indispensable.

However, not every valuable dataset or platform constitutes an essential facility. The stringent conditions of the doctrine remain relevant.

15. IMS Health v Commission (CJEU, 2004)

IMS Health concerned access to a commercially valuable data structure and the exceptional circumstances under which refusal to license intellectual property could constitute abuse.

Relevance

This case is particularly significant for strategic foresight platforms because predictive systems may depend upon:

• proprietary databases;
• specialised taxonomies;
• data architectures;
• forecasting models; and
• commercially protected information structures.

The case illustrates the tension between innovation incentives and access obligations.

16. Intel v Commission (CJEU, 2017)

The Intel litigation concerned conditional rebates and the assessment of exclusionary conduct by a dominant undertaking.

Relevance

A dominant foresight platform could potentially use discounts or contractual incentives to lock customers into an ecosystem.

Examples include:

• rebates conditioned on exclusivity;
• bundled forecasting and data services;
• discounts for adopting the platform's entire analytics stack;
• contractual restrictions on using competing forecasts.

The competitive assessment would depend on the specific conduct and its effects.

17. Google Android (European Commission, 2018; General Court, 2022)

The Android case involved Google's contractual arrangements concerning mobile devices, applications and search services.

Relevance

The case illustrates how a dominant digital ecosystem may use contractual arrangements across adjacent products to reinforce its position.

For foresight platforms, analogous concerns could arise where a provider bundles:

data + forecasting model + cloud infrastructure + API + recommendation service.

This may make it difficult for customers to substitute individual components.

18. Apple App Store Cases and Digital-Gatekeeper Regulation

Competition authorities in several jurisdictions have increasingly examined digital ecosystems involving:

• app distribution;
• payment systems;
• interoperability;
• access restrictions;
• self-preferencing; and
• platform fees.

Relevance

Strategic foresight platforms could similarly evolve from stand-alone analytical tools into gatekeeper ecosystems.

A platform could control access to:

1. data;
2. prediction models;
3. APIs;
4. distribution;
5. enterprise integrations; and
6. downstream recommendation markets.

This makes ecosystem regulation increasingly relevant.

19. Potential Abuses by Strategic Foresight Platforms

A. Exclusive dealing

A platform may require customers to use only its forecasting service.

B. Tying

The platform could condition access to valuable data upon purchasing its forecasting product.

C. Bundling

Forecasting could be bundled with cloud, search, financial intelligence or enterprise software.

D. Self-preferencing

The platform could favour its own predictions or downstream services.

E. Discriminatory access

Third-party providers could receive inferior access to:

• APIs;
• datasets;
• computing resources;
• interfaces; or
• platform functionality.

F. Predatory pricing

A large ecosystem could temporarily subsidise forecasting services to eliminate specialised competitors.

G. Margin squeeze

A vertically integrated platform could charge competitors high prices for essential data while competing downstream with its own forecasting product.

H. Refusal to deal

Access to unique datasets or infrastructure could potentially become a competition-law issue under exceptional circumstances.

20. Merger Control

Strategic foresight platforms could also create novel merger problems.

Consider a merger between:

Platform A: massive consumer dataset
Platform B: advanced forecasting model.

Even if neither company has overwhelming market share individually, the transaction could combine:

data advantage + prediction capability + distribution.

Competition authorities may therefore examine:

• data overlap;
• potential competitors;
• nascent competition;
• innovation competition;
• access to datasets;
• AI capabilities;
• interoperability;
• vertical foreclosure;
• ecosystem effects.

21. Killer Acquisitions and Nascent Foresight Competitors

A dominant platform could acquire a small start-up developing:

• superior forecasting models;
• alternative data architecture;
• specialised industry predictions;
• open-source forecasting tools; or
• privacy-preserving analytics.

The start-up may have little current revenue but substantial future competitive significance.

Consequently, conventional turnover thresholds may not always capture the competitive importance of such transactions.

22. Future Regulation

Future regulation is likely to move beyond traditional market-share analysis.

A. Data-access regulation

Regulators may require controlled access to certain categories of data where necessary for effective competition.

B. Interoperability

Platforms could be required to support interoperable:

• APIs;
• data formats;
• model interfaces;
• forecasting outputs.

C. Transparency

Regulators may require disclosure concerning:

• important ranking criteria;
• recommendation logic;
• material data inputs;
• conflicts of interest.

This would not necessarily require disclosure of the entire algorithm.

D. Algorithmic auditing

Independent audits could examine whether a platform:

• systematically favours its own services;
• discriminates against competitors;
• facilitates coordination; or
• manipulates access.

E. Data portability

Enterprise customers could receive usable copies of their historical forecasting and operational data.

F. Switching rights

Regulation could facilitate migration between competing forecasting platforms.

23. Strategic Foresight as an Essential Facility

The doctrine could become relevant where a platform controls infrastructure that is:

1. genuinely indispensable;
2. not reasonably reproducible;
3. unavailable through realistic alternatives; and
4. necessary for effective competition.

Examples might eventually include highly specialised real-time datasets.

However, courts and regulators would need to balance access obligations against:

• intellectual property;
• privacy;
• cybersecurity;
• investment incentives;
• confidentiality;
• data-protection requirements.

24. Competition Between Forecasting Models

Future competition may occur not merely between companies but between prediction architectures.

Possible competing models include:

• proprietary AI;
• open-source AI;
• federated forecasting;
• decentralised prediction systems;
• industry-specific models;
• human-AI hybrid systems.

Competition law may therefore need to preserve model diversity.

25. The Problem of Forecasting Bias

A dominant platform could theoretically manipulate forecasts to favour its own commercial interests.

For example, a platform operating both:

• a forecasting service; and
• an investment or trading business

could possess incentives to generate forecasts benefiting its downstream operations.

Competition authorities may therefore examine vertical conflicts of interest.

26. Forecasting as a New Form of Gatekeeping

Traditional gatekeepers controlled access to:

• customers;
• infrastructure;
• distribution;
• payment systems.

Strategic foresight platforms could control access to something different:

knowledge about the future market.

If businesses increasingly rely on one platform's predictions to make investment and commercial decisions, that platform could exercise significant informational gatekeeping power.

27. Regulatory Challenges

Future regulators will face several difficulties.

1. Proving causation

It may be difficult to establish whether a forecast caused competitive harm.

2. Algorithmic opacity

Prediction systems can be difficult to interpret.

3. Rapid innovation

Competition authorities may regulate technologies that change quickly.

4. Data confidentiality

Effective investigation may require access to sensitive datasets.

5. False positives

Not every superior forecasting capability is anticompetitive.

6. Innovation incentives

Excessive compulsory access could reduce incentives to develop expensive forecasting infrastructure.

28. Proposed Regulatory Model

A future regulatory framework could be organised around five layers.

This would permit regulation to address the entire foresight ecosystem rather than only the final forecasting product.

29. Competition Compliance for Platforms

Strategic foresight providers should consider:

• independent algorithmic audits;
• competition-law review of data-sharing arrangements;
• safeguards against competitor information exchange;
• clear access policies;
• non-discriminatory APIs;
• restrictions on use of competitively sensitive information;
• internal merger-control review;
• documentation of model changes;
• compliance monitoring of recommendation systems.

30. Competition Risks for Users

Businesses using these platforms should also assess whether they are unintentionally facilitating coordination.

For example, competing companies should exercise caution where the same platform provides them with:

• future price recommendations;
• production forecasts;
• capacity forecasts;
• inventory targets;
• competitor-specific intelligence.

Using a common algorithm does not automatically establish an infringement, but the information architecture and communications surrounding its use may become legally important.

31. Six Core Doctrinal Connections

The competition-law treatment of strategic foresight platforms can therefore be summarised through six traditional doctrines:

1. Abuse of dominance – exploitation of informational or infrastructural power.
2. Information exchange – exchange of competitively sensitive future information.
3. Algorithmic coordination – common systems facilitating parallel conduct.
4. Essential facilities – possible access to indispensable data or infrastructure.
5. Tying and bundling – combining forecasts with other digital services.
6. Merger control – acquisition of datasets, models or nascent competitors.

32. Emerging Concept: Predictive Market Power

A useful future competition-law concept is predictive market power.

Traditional market power concerns the ability to:

raise prices, reduce output or worsen terms.

Predictive market power may instead concern the ability to:

anticipate, shape or influence the future conditions under which competitors make decisions.

A platform with exceptional predictive capability could potentially influence:

• investment;
• capacity;
• pricing;
• procurement;
• innovation;
• market entry.

The concept should, however, remain an analytical framework rather than an automatic legal category.

33. Key Case-Law Takeaways

Conclusion

Strategic foresight platforms represent a potentially important next stage in digital competition. Their competitive significance does not arise merely from providing information. It arises from the combination of data, predictive models, computational infrastructure, network effects and decision-making interfaces.

Competition law will increasingly have to address whether a platform:

• controls indispensable predictive inputs;
• excludes competing forecasting systems;
• favours its own downstream services;
• imposes restrictive contractual arrangements;
• facilitates coordination among competitors;
• acquires nascent predictive competitors; or
• uses data and forecasting capabilities to extend dominance across adjacent markets.

The principal regulatory challenge will be to distinguish legitimate superior prediction and innovation from conduct that converts predictive capability into durable exclusionary power.